GPT-3 token limit exceeded error when using OpenAI API

Other Questions OpenAI
1

I’m running into a token limit issue when calling the OpenAI API. My input text is about 1360 tokens long (checked this with their tokenizer tool).

Here’s the code I’m using in Node.js:

const result = await openaiClient.completions.create({
  model: 'text-davinci-003',
  prompt: userInput,
  max_tokens: 4000,
  temperature: 0.2
});

The weird thing is that when I test the same prompt in OpenAI’s playground, it works fine and only uses 1374 tokens total. But when I use the API, I get this error:

{
  message: "This model's maximum context length is 4097 tokens, however you requested 5360 tokens (1360 in your prompt; 4000 for the completion). Please reduce your prompt; or completion length.",
  type: 'invalid_request_error'
}

Has anyone faced this before? How did you fix it?

2

Same exact frustration here when I moved from playground to API! The playground auto-calculates how many tokens are left for the response, but you’re hardcoding max_tokens at 4000 no matter how long your prompt is. I fixed it by calculating remaining tokens first: const remainingTokens = 4097 - promptTokens - 100; then using that for max_tokens. That extra 100 tokens covers any differences between your tokenizer and what the API actually counts. Haven’t hit a token limit error since.

3

Got burned by this in production last year. The token limit counts prompt AND completion together, not separately. What really threw me off was that different tokenization libraries don’t always match OpenAI’s internal counting. I ended up wrapping my API calls with error handling that auto-retries with lower max_tokens when it hits the limit. Just catch that specific error and cut max_tokens by 25% on retry. Way easier than trying to calculate tokens perfectly every time, especially with variable user input. The fallback approach handles edge cases much better than getting the math perfect upfront.

4

The error message is doing the math for you. You’re asking for 1360 tokens (prompt) + 4000 tokens (completion) = 5360 tokens total. But text-davinci-003 maxes out at 4097 tokens for everything - input AND output combined, not just the response.

The playground works because it auto-adjusts max_tokens based on your prompt length. With the API, you’ve got to do this math yourself. Set max_tokens to around 2700 instead of 4000. That’ll give you plenty of space for the response while staying under 4097.

I hit this exact same issue last month and wasted hours debugging before I realized the token limit covers both input and output together.

5

Ran into this exact issue moving from playground to production API calls. Super annoying when you’ve got different prompt lengths throughout your app. Here’s what fixed it for me: I added a token counter that runs before each API call and sets max_tokens dynamically based on what’s left. I use Math.max(50, 4097 - estimatedPromptTokens - 20) so I never hit zero or negative tokens, plus it keeps a small buffer. Different tokenization methods can give slightly different counts, so that buffer prevents edge case failures. Been rock solid across thousands of API calls with all kinds of prompt sizes.

6

this got me so bad when i started using the api! the playground’s misleading - it does all the token math for u, but the api makes u handle it yourself. just drop ur max_tokens to 2500-2700 depending on ur prompt length. took me hours of frustration to figure that out.

7

yeah, that’s a classic openai api gotcha. the playground auto-calculates remaining tokens for you, but the api doesn’t. you’re doing 1360 + 4000, which blows past the 4097 limit. try max_tokens: 4097 - prompt_tokens - 50 to leave some buffer room.

8

Look, everyone’s giving you manual calculation solutions, but you’ll hit this problem constantly with different prompts and models. I deal with this at work all the time.

You need dynamic token management that adapts automatically. Every prompt change or model switch means you’re back doing math and updating code.

I fixed this by setting up an automation flow for OpenAI API calls. It calculates available tokens automatically, splits long prompts when needed, and retries with adjusted parameters if it hits limits.

The flow monitors token usage in real time and adjusts max_tokens based on actual model limits and prompt length. No hardcoded values or manual calculations. Plus it logs everything so you can see what’s happening.

Took 30 minutes to set up and I haven’t had a single token limit error since. Way better than fixing this in your code every time.

Check it out here: https://latenode.com